New Approaches to Estimating Immigrant Documentation Status in Survey Data
Bibliographic record
Abstract
Approximately a quarter of the 43 million immigrants living in the United States are thought to be undocumented. Yet, the lack of accurate population-level information about undocumented immigrants provides fertile ground for public misconceptions, political and media hype, and false claims. The goal is to determine how well descriptions of the undocumented population are likely to mirror the reality of undocumented immigrants’ lives in the US. We compare (1) the distribution of the population by documentation status and (2) distributions of the characteristics of undocumented and documented immigrants produced by two methods. The first method (the “decomposition method”) is a commonly used strategy used in previous work and the second method is an alternative, independent method developed in this article. We used the Survey of Income and Program Participation (SIPP) and the Los Angeles Family and Neighborhood Survey (LAFANS). The existing decomposition method works reasonably well if the data contains information on whether respondents are naturalized citizens or and lawful permanent residents. However, when these variables are missing or problematic, the decomposition method produces biased results. The actual undocumented population in the US may be even more socioeconomically disadvantaged than studies based on existing decomposition methods indicate. This article evaluates methods to conduct reasonably accurate nationally representative, policy relevant research on the lives of undocumented immigrants without potentially jeopardizing members of this vulnerable population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.104 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".